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extern crate ndarray;
extern crate ndarray_rand;
use ndarray::prelude::*;
use ndarray_rand::rand_distr::Uniform;
use ndarray_rand::RandomExt;
use std::sync::RwLock;
pub trait Layer1d {
/// Feeds forward the 1d array through the layer.
///
/// # Arguments
///
/// * `input_array`: Has to be the same size as the input size of the layer else will panic
///
/// returns: `Array1<f64>`
///
/// # Examples
///
/// ```
/// use ducky_learn::layers::*;
/// use ndarray::{arr1, arr2};
///
/// let layer = Dense1d::from(
/// |x| x, // Activation function that is does nothing
/// |x| x.map(|i| 1f64), // Derivative of Activation function
/// arr2(&[[1., 1.], [1., 1.]]), // 2x2 array
/// arr1(&[1., 1.]) // len 2
/// );
///
/// let output = layer.pass(arr1(&[1., 1.]));
///
/// ```
fn pass(&self, input_array: Array1<f64>) -> (Array1<f64>, Array1<f64>); // TODO: update doc
}
pub struct Dense1d {
activation: fn(Array1<f64>) -> Array1<f64>,
deriv_activation: fn(Array1<f64>) -> Array1<f64>,
weights: RwLock<Array2<f64>>,
bias: RwLock<Array1<f64>>,
}
impl Dense1d {
/// Create Dense1d layer with full control over every part of the layer
///
/// # Arguments
///
/// * `activation`: Activation function of whole 1d array
/// * `weights`: 2d array that has to be of shape( output, input )
/// * `bias`: 1d array of basis that has to be the size of the output
///
/// returns: `Dense1d`
///
/// # Examples
///
/// ```
/// use ducky_learn::layers::*;
/// use ndarray::{arr1, arr2};
///
/// let layer = Dense1d::from(
/// |x| x, // Activation function that is does nothing
/// |x| x.map(|i| 1f64), // Derivative of Activation function
/// arr2(&[[1., 1.], [1., 1.]]), // 2x2 array
/// arr1(&[1., 1.]) // len 2
/// );
/// ```
pub fn from(
activation: fn(Array1<f64>) -> Array1<f64>,
deriv_activation: fn(Array1<f64>) -> Array1<f64>,
weights: Array2<f64>,
bias: Array1<f64>,
) -> Self {
Self {
activation,
deriv_activation,
weights: RwLock::new(weights),
bias: RwLock::new(bias),
}
}
/// Create randomly set weights and bias's for the dense1d layer.
/// Creates weights and bias's using a normal distribution from -1. -> 1.
///
/// # Arguments
///
/// * `input_size`: size of input array
/// * `layer_size`: number of nodes in the layer
/// * `activation_fn`: activation function for the layer
///
/// returns: `Dense1d`
///
/// # Examples
///
/// ```
/// use ducky_learn::layers::*;
/// use ndarray::{arr1, arr2};
///
/// let layer = Dense1d::new(5, 10, |x| x, |x| x);
/// let input_array = arr1(&[
/// 1., 1., 1., 1., 1.
/// ]);
///
/// layer.pass(input_array);
/// ```
pub fn new(
input_size: usize,
layer_size: usize,
activation_fn: fn(Array1<f64>) -> Array1<f64>,
deriv_activation_fn: fn(Array1<f64>) -> Array1<f64>,
) -> Self {
Self {
activation: activation_fn,
deriv_activation: deriv_activation_fn,
weights: RwLock::new(Array2::random(
(layer_size, input_size),
Uniform::new(-1., 1.),
)),
bias: RwLock::new(Array1::random(layer_size, Uniform::new(-1., 1.))),
}
}
}
impl Layer1d for Dense1d {
fn pass(&self, input_array: Array1<f64>) -> (Array1<f64>, Array1<f64>) {
let weights = self.weights.read().unwrap();
let bias = self.bias.read().unwrap();
assert_eq!(
weights.shape()[1],
input_array.shape()[0],
"Layer input size is {}, \
Layer was given size of {}",
weights.shape()[1],
input_array.shape()[0]
);
let z = weights.dot(&input_array) + &*bias;
let a = (self.activation)(z.clone());
(z, a)
}
}
#[cfg(test)]
mod layers_tests {
use super::*;
use ndarray::*;
use crate::activations::*;
#[test]
fn dense1d_pass_arr1_1() {
let layer = Dense1d::from(
|x| x,
|x| x,
arr2(&[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]),
arr1(&[1., 1., 1.]),
);
let input_array = arr1(&[1., 1., 1.]);
assert_eq!(layer.pass(input_array).1, arr1(&[4., 4., 4.]))
}
#[test]
fn dense1d_pass_arr1_2() {
let layer = Dense1d::from(
|x| x,
|x| x,
arr2(&[
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],
]),
arr1(&[1., 1., 1.]),
);
let input_array = arr1(&[1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]);
assert_eq!(layer.pass(input_array).1, arr1(&[13.0, 13.0, 13.0]))
}
#[test]
#[should_panic]
fn dense1d_pass_arr1_diff_size() {
let layer = Dense1d::from(
|x| x,
|x| x,
arr2(&[[1., 1., 1., 1.], [1., 1., 1., 1.]]),
arr1(&[0., 0.]),
);
let input_array = arr1(&[1.]);
layer.pass(input_array);
}
#[test]
fn dense1d_new() {
let layer = Dense1d::new(5, 10, |x| x, |x| x);
let input_array = arr1(&[1., 1., 1., 1., 1.]);
layer.pass(input_array);
}
#[test]
fn dense1d_activation() {
let layer = Dense1d::from(
relu_1d,
deriv_relu_1d,
arr2(&[[1., 1., 1.], [1., 1., 1.], [1., 1., 1.]]),
arr1(&[-10., -10., 1.]),
);
let input_array = arr1(&[1., 1., 1.]);
assert_eq!(layer.pass(input_array).1, arr1(&[0., 0., 4.]))
}
}